
import pandas as pd
import numpy as np
import warnings
import re
import nltk
import json
from sklearn.model_selection import train_test_split
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.svm import LinearSVC
from sklearn.naive_bayes import MultinomialNB
from sklearn.linear_model import LogisticRegression
import xgboost as xgb
from sklearn.ensemble import RandomForestClassifier
from sklearn import metrics
from sklearn.model_selection import GridSearchCV, RandomizedSearchCV, RepeatedStratifiedKFold
from sklearn.metrics import accuracy_score, roc_curve,roc_auc_score, \
                            classification_report, confusion_matrix, \
                            precision_recall_curve, precision_score, \
                            f1_score, fbeta_score
from sklearn.metrics import plot_confusion_matrix
import matplotlib.pyplot as plt
from imblearn.over_sampling import RandomOverSampler
import scikitplot as skplt
import shap
import plotly.express as px
from imblearn.over_sampling import SMOTE
from sklearn.inspection import PartialDependenceDisplay


pd.set_option('display.max_columns', 500)
pd.set_option('display.max_rows', 5000)


def get_models_score(classifiers, X_train, Y_train):
    
    for classifier in classifiers:

        classifier.fit(X_train, Y_train)  
        y_pred = classifier.predict(X_test)
        print(classifier)
        print("model score: %.3f" % classifier.score(X_test, Y_test))
        print("Accuracy:",metrics.accuracy_score(Y_test, y_pred))
        
def get_model_report(Y_test, y_pred):
    
    print("Classification Report")
    print(classification_report(Y_test, y_pred))
    
def get_confusion_matrix(model, X_test, Y_test):
    
    titles_options = [("Confusion matrix, without normalization", None),
                  ("Normalized confusion matrix", 'true')]

    for title, normalize in titles_options:
        
        disp = plot_confusion_matrix(model, X_test, Y_test,

                                     cmap=plt.cm.Blues,
                                     normalize=normalize)
        disp.ax_.set_title(title)

        print(title)
        print(disp.confusion_matrix)

    plt.show()
    
    
def transform_ratings(row):
    
    if row['stars_x'] <= 3:
        return 0
    else:
        return 1
    
def get_roc_curves(ytest, yhat):
    
    
    fpr, tpr, thresholds = roc_curve(ytest, yhat)
    
    plt.plot([0,1], [0,1], linestyle='--', label='No Skill')
    plt.plot(fpr, tpr, marker='.', label='LGBM')
   
    plt.xlabel('False Positive Rate')
    plt.ylabel('True Positive Rate')
    plt.legend()
    
    plt.show()
    
    gmeans = np.sqrt(tpr * (1-fpr))
    
    ix = np.argmax(gmeans)
    print('Best Threshold=%f, G-Mean=%.3f' % (thresholds[ix], gmeans[ix]))
    
    plt.plot([0,1], [0,1], linestyle='--', label='No Skill')
    plt.plot(fpr, tpr, marker='.', label='LGBM')
    plt.scatter(fpr[ix], tpr[ix], s=100, marker='o', color='black', label='Best')
    plt.xlabel('False Positive Rate')
    plt.ylabel('True Positive Rate')
    plt.legend()
    plt.show()

def get_precission_recall_curve(ytest):
    
    no_skill = len(ytest[ytest==1]) / len(ytest)
    plt.plot([0,1], [no_skill,no_skill], linestyle='--', label='No Skill')
    plt.plot(recall, precision, marker='.', label='LGBM')
    plt.xlabel('Recall')
    plt.ylabel('Precision')
    plt.legend()
    plt.show()
    
    
    fscore = (2 * precision * recall) / (precision + recall)
    ix = np.argmax(fscore)
    print('Best Threshold=%f, F-Score=%.3f' % (thresholds[ix], fscore[ix]))
    no_skill = len(ytest[ytest==1]) / len(ytest)
    plt.plot([0,1], [no_skill,no_skill], linestyle='--', label='No Skill')
    plt.plot(recall, precision, marker='.', label='LGBM')
    plt.scatter(recall[ix], precision[ix], s=100, marker='o', color='black', label='Best')
    plt.xlabel('Recall')
    plt.ylabel('Precision')
    plt.legend()
    plt.show()
    
def get_pie_chart(df):
    
    fig_1 = px.pie(df,
               names='index',
               height=400,
               width=600,
               hole=0.4,
               title='Nightlife Businesses Stars Distribution',
               values='percent',
               color_discrete_sequence=['#2AA10F','#92E000','#E1FF00','#F58B00','#DE3700']
               )

    fig_1.update_layout(legend=dict(orientation='h', yanchor='bottom', y=-0.2, xanchor='center', x=0.5))
    fig_1.show()


nightlife_businesses_path = '../data/Processed/df_nightlife_PA_reviews.csv'
seed = 123456
beta = 2
ind = 6


warnings.filterwarnings('ignore')


nightlife_businesses_PA = pd.read_csv(nightlife_businesses_path)
nightlife_businesses_PA.head()


print("Total No. of Reviews: {}".format(nightlife_businesses_PA.shape[0]))

Total No. of Reviews: 261678


nightlife_businesses_PA_stars = nightlife_businesses_PA["stars_x"].value_counts(normalize=True).mul(100).rename('percent').reset_index()
nightlife_businesses_PA_count = nightlife_businesses_PA["stars_x"].value_counts().reset_index()
nightlife_businesses_PA_count_pct = pd.merge(nightlife_businesses_PA_stars, nightlife_businesses_PA_count, on=['index'], how='inner')
nightlife_businesses_PA_count_pct


get_pie_chart(nightlife_businesses_PA_count_pct)


nightlife_businesses_PA["stars_x"].value_counts()

5.0    98064
4.0    74091
3.0    36519
1.0    28468
2.0    24536
Name: stars_x, dtype: int64


nightlife_businesses_PA['stars_x'] = nightlife_businesses_PA['stars_x'].round().astype(int)
nightlife_businesses_PA.head(50)


nightlife_businesses_PA["stars_x"].value_counts()

5    98064
4    74091
3    36519
1    28468
2    24536
Name: stars_x, dtype: int64


stars_ratings = [nightlife_businesses_PA['stars_x'] for review in nightlife_businesses_PA]
stars_ratings

[0         5
 1         3
 2         4
 3         5
 4         5
          ..
 261673    2
 261674    5
 261675    5
 261676    5
 261677    5
 Name: stars_x, Length: 261678, dtype: int64,
 0         5
 1         3
 2         4
 3         5
 4         5
          ..
 261673    2
 261674    5
 261675    5
 261676    5
 261677    5
 Name: stars_x, Length: 261678, dtype: int64,
 0         5
 1         3
 2         4
 3         5
 4         5
          ..
 261673    2
 261674    5
 261675    5
 261676    5
 261677    5
 Name: stars_x, Length: 261678, dtype: int64,
 0         5
 1         3
 2         4
 3         5
 4         5
          ..
 261673    2
 261674    5
 261675    5
 261676    5
 261677    5
 Name: stars_x, Length: 261678, dtype: int64,
 0         5
 1         3
 2         4
 3         5
 4         5
          ..
 261673    2
 261674    5
 261675    5
 261676    5
 261677    5
 Name: stars_x, Length: 261678, dtype: int64,
 0         5
 1         3
 2         4
 3         5
 4         5
          ..
 261673    2
 261674    5
 261675    5
 261676    5
 261677    5
 Name: stars_x, Length: 261678, dtype: int64,
 0         5
 1         3
 2         4
 3         5
 4         5
          ..
 261673    2
 261674    5
 261675    5
 261676    5
 261677    5
 Name: stars_x, Length: 261678, dtype: int64,
 0         5
 1         3
 2         4
 3         5
 4         5
          ..
 261673    2
 261674    5
 261675    5
 261676    5
 261677    5
 Name: stars_x, Length: 261678, dtype: int64,
 0         5
 1         3
 2         4
 3         5
 4         5
          ..
 261673    2
 261674    5
 261675    5
 261676    5
 261677    5
 Name: stars_x, Length: 261678, dtype: int64,
 0         5
 1         3
 2         4
 3         5
 4         5
          ..
 261673    2
 261674    5
 261675    5
 261676    5
 261677    5
 Name: stars_x, Length: 261678, dtype: int64,
 0         5
 1         3
 2         4
 3         5
 4         5
          ..
 261673    2
 261674    5
 261675    5
 261676    5
 261677    5
 Name: stars_x, Length: 261678, dtype: int64,
 0         5
 1         3
 2         4
 3         5
 4         5
          ..
 261673    2
 261674    5
 261675    5
 261676    5
 261677    5
 Name: stars_x, Length: 261678, dtype: int64,
 0         5
 1         3
 2         4
 3         5
 4         5
          ..
 261673    2
 261674    5
 261675    5
 261676    5
 261677    5
 Name: stars_x, Length: 261678, dtype: int64,
 0         5
 1         3
 2         4
 3         5
 4         5
          ..
 261673    2
 261674    5
 261675    5
 261676    5
 261677    5
 Name: stars_x, Length: 261678, dtype: int64,
 0         5
 1         3
 2         4
 3         5
 4         5
          ..
 261673    2
 261674    5
 261675    5
 261676    5
 261677    5
 Name: stars_x, Length: 261678, dtype: int64,
 0         5
 1         3
 2         4
 3         5
 4         5
          ..
 261673    2
 261674    5
 261675    5
 261676    5
 261677    5
 Name: stars_x, Length: 261678, dtype: int64,
 0         5
 1         3
 2         4
 3         5
 4         5
          ..
 261673    2
 261674    5
 261675    5
 261676    5
 261677    5
 Name: stars_x, Length: 261678, dtype: int64,
 0         5
 1         3
 2         4
 3         5
 4         5
          ..
 261673    2
 261674    5
 261675    5
 261676    5
 261677    5
 Name: stars_x, Length: 261678, dtype: int64,
 0         5
 1         3
 2         4
 3         5
 4         5
          ..
 261673    2
 261674    5
 261675    5
 261676    5
 261677    5
 Name: stars_x, Length: 261678, dtype: int64,
 0         5
 1         3
 2         4
 3         5
 4         5
          ..
 261673    2
 261674    5
 261675    5
 261676    5
 261677    5
 Name: stars_x, Length: 261678, dtype: int64,
 0         5
 1         3
 2         4
 3         5
 4         5
          ..
 261673    2
 261674    5
 261675    5
 261676    5
 261677    5
 Name: stars_x, Length: 261678, dtype: int64,
 0         5
 1         3
 2         4
 3         5
 4         5
          ..
 261673    2
 261674    5
 261675    5
 261676    5
 261677    5
 Name: stars_x, Length: 261678, dtype: int64,
 0         5
 1         3
 2         4
 3         5
 4         5
          ..
 261673    2
 261674    5
 261675    5
 261676    5
 261677    5
 Name: stars_x, Length: 261678, dtype: int64,
 0         5
 1         3
 2         4
 3         5
 4         5
          ..
 261673    2
 261674    5
 261675    5
 261676    5
 261677    5
 Name: stars_x, Length: 261678, dtype: int64,
 0         5
 1         3
 2         4
 3         5
 4         5
          ..
 261673    2
 261674    5
 261675    5
 261676    5
 261677    5
 Name: stars_x, Length: 261678, dtype: int64,
 0         5
 1         3
 2         4
 3         5
 4         5
          ..
 261673    2
 261674    5
 261675    5
 261676    5
 261677    5
 Name: stars_x, Length: 261678, dtype: int64,
 0         5
 1         3
 2         4
 3         5
 4         5
          ..
 261673    2
 261674    5
 261675    5
 261676    5
 261677    5
 Name: stars_x, Length: 261678, dtype: int64,
 0         5
 1         3
 2         4
 3         5
 4         5
          ..
 261673    2
 261674    5
 261675    5
 261676    5
 261677    5
 Name: stars_x, Length: 261678, dtype: int64,
 0         5
 1         3
 2         4
 3         5
 4         5
          ..
 261673    2
 261674    5
 261675    5
 261676    5
 261677    5
 Name: stars_x, Length: 261678, dtype: int64,
 0         5
 1         3
 2         4
 3         5
 4         5
          ..
 261673    2
 261674    5
 261675    5
 261676    5
 261677    5
 Name: stars_x, Length: 261678, dtype: int64,
 0         5
 1         3
 2         4
 3         5
 4         5
          ..
 261673    2
 261674    5
 261675    5
 261676    5
 261677    5
 Name: stars_x, Length: 261678, dtype: int64,
 0         5
 1         3
 2         4
 3         5
 4         5
          ..
 261673    2
 261674    5
 261675    5
 261676    5
 261677    5
 Name: stars_x, Length: 261678, dtype: int64,
 0         5
 1         3
 2         4
 3         5
 4         5
          ..
 261673    2
 261674    5
 261675    5
 261676    5
 261677    5
 Name: stars_x, Length: 261678, dtype: int64,
 0         5
 1         3
 2         4
 3         5
 4         5
          ..
 261673    2
 261674    5
 261675    5
 261676    5
 261677    5
 Name: stars_x, Length: 261678, dtype: int64,
 0         5
 1         3
 2         4
 3         5
 4         5
          ..
 261673    2
 261674    5
 261675    5
 261676    5
 261677    5
 Name: stars_x, Length: 261678, dtype: int64,
 0         5
 1         3
 2         4
 3         5
 4         5
          ..
 261673    2
 261674    5
 261675    5
 261676    5
 261677    5
 Name: stars_x, Length: 261678, dtype: int64,
 0         5
 1         3
 2         4
 3         5
 4         5
          ..
 261673    2
 261674    5
 261675    5
 261676    5
 261677    5
 Name: stars_x, Length: 261678, dtype: int64,
 0         5
 1         3
 2         4
 3         5
 4         5
          ..
 261673    2
 261674    5
 261675    5
 261676    5
 261677    5
 Name: stars_x, Length: 261678, dtype: int64,
 0         5
 1         3
 2         4
 3         5
 4         5
          ..
 261673    2
 261674    5
 261675    5
 261676    5
 261677    5
 Name: stars_x, Length: 261678, dtype: int64,
 0         5
 1         3
 2         4
 3         5
 4         5
          ..
 261673    2
 261674    5
 261675    5
 261676    5
 261677    5
 Name: stars_x, Length: 261678, dtype: int64,
 0         5
 1         3
 2         4
 3         5
 4         5
          ..
 261673    2
 261674    5
 261675    5
 261676    5
 261677    5
 Name: stars_x, Length: 261678, dtype: int64,
 0         5
 1         3
 2         4
 3         5
 4         5
          ..
 261673    2
 261674    5
 261675    5
 261676    5
 261677    5
 Name: stars_x, Length: 261678, dtype: int64,
 0         5
 1         3
 2         4
 3         5
 4         5
          ..
 261673    2
 261674    5
 261675    5
 261676    5
 261677    5
 Name: stars_x, Length: 261678, dtype: int64,
 0         5
 1         3
 2         4
 3         5
 4         5
          ..
 261673    2
 261674    5
 261675    5
 261676    5
 261677    5
 Name: stars_x, Length: 261678, dtype: int64,
 0         5
 1         3
 2         4
 3         5
 4         5
          ..
 261673    2
 261674    5
 261675    5
 261676    5
 261677    5
 Name: stars_x, Length: 261678, dtype: int64,
 0         5
 1         3
 2         4
 3         5
 4         5
          ..
 261673    2
 261674    5
 261675    5
 261676    5
 261677    5
 Name: stars_x, Length: 261678, dtype: int64,
 0         5
 1         3
 2         4
 3         5
 4         5
          ..
 261673    2
 261674    5
 261675    5
 261676    5
 261677    5
 Name: stars_x, Length: 261678, dtype: int64,
 0         5
 1         3
 2         4
 3         5
 4         5
          ..
 261673    2
 261674    5
 261675    5
 261676    5
 261677    5
 Name: stars_x, Length: 261678, dtype: int64,
 0         5
 1         3
 2         4
 3         5
 4         5
          ..
 261673    2
 261674    5
 261675    5
 261676    5
 261677    5
 Name: stars_x, Length: 261678, dtype: int64,
 0         5
 1         3
 2         4
 3         5
 4         5
          ..
 261673    2
 261674    5
 261675    5
 261676    5
 261677    5
 Name: stars_x, Length: 261678, dtype: int64,
 0         5
 1         3
 2         4
 3         5
 4         5
          ..
 261673    2
 261674    5
 261675    5
 261676    5
 261677    5
 Name: stars_x, Length: 261678, dtype: int64,
 0         5
 1         3
 2         4
 3         5
 4         5
          ..
 261673    2
 261674    5
 261675    5
 261676    5
 261677    5
 Name: stars_x, Length: 261678, dtype: int64,
 0         5
 1         3
 2         4
 3         5
 4         5
          ..
 261673    2
 261674    5
 261675    5
 261676    5
 261677    5
 Name: stars_x, Length: 261678, dtype: int64,
 0         5
 1         3
 2         4
 3         5
 4         5
          ..
 261673    2
 261674    5
 261675    5
 261676    5
 261677    5
 Name: stars_x, Length: 261678, dtype: int64,
 0         5
 1         3
 2         4
 3         5
 4         5
          ..
 261673    2
 261674    5
 261675    5
 261676    5
 261677    5
 Name: stars_x, Length: 261678, dtype: int64]


keywords = nightlife_businesses_PA['keywords'] 
keywords

0         ['Tremendous', 'service', 'Big', 'shout', 'Dou...
1         ['good', 'place', 'lofty', 'prices', 'proporti...
2         ['bar', 'area', 'upscale', 'cities', 'restaura...
3         ['prime', 'rib', 'steak', 'joints', 'experience']
4         ['name', 'Best', 'Prime', 'Rib', 'town', 'Serv...
                                ...                        
261673    ['family', 'special', 'occasion', 'help', 'dis...
261674                                     ['open', 'spot']
261675    ['beautiful', 'birthday', 'Lark', 'staff', 'de...
261676                    ['good', 'Lark', 'anyone', 'get']
261677    ['food', 'perfect', 'rare', 'restaurant', 'goo...
Name: keywords, Length: 261678, dtype: object


# This vectorizer breaks text into single words and bi-grams
# and then calculates the TF-IDF representation
vectorizer = TfidfVectorizer(ngram_range=(1,1))

# the 'fit' builds up the vocabulary from all the reviews
# while the 'transform' step turns each indivdual text into
# a matrix of numbers.
vectors = vectorizer.fit_transform(keywords)


vectors

<261678x24548 sparse matrix of type '<class 'numpy.float64'>'
	with 1335280 stored elements in Compressed Sparse Row format>


X_train, X_test, Y_train, Y_test = train_test_split(vectors, stars_ratings[1], test_size=0.20, random_state=seed, 
                                                    shuffle =False,)


classifiers = [
    LogisticRegression(),
    MultinomialNB(),
    RandomForestClassifier(),
    LinearSVC()
    ]

get_models_score(classifiers, X_train, Y_train)

LogisticRegression()
model score: 0.485
Accuracy: 0.48492433506572913
MultinomialNB()
model score: 0.463
Accuracy: 0.4634095077957811
RandomForestClassifier()
model score: 0.465
Accuracy: 0.4652247019260165
LinearSVC()
model score: 0.473
Accuracy: 0.47330709263222254


LR = LogisticRegression()


LR = LogisticRegression(penalty='l2', C=1.0, random_state=seed, n_jobs=2)

LR.fit(X_train, Y_train)
y_pred = LR.predict(X_test)

print("model score: %.3f" % LR.score(X_test, Y_test))
print("Accuracy:",metrics.accuracy_score(Y_test, y_pred))

<frozen importlib._bootstrap>:228: RuntimeWarning: scipy._lib.messagestream.MessageStream size changed, may indicate binary incompatibility. Expected 56 from C header, got 64 from PyObject

model score: 0.485
Accuracy: 0.48492433506572913

/Users/ignaciogonzalez/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(


get_model_report(Y_test, y_pred)

Classification Report
              precision    recall  f1-score   support

           1       0.56      0.52      0.54      6114
           2       0.31      0.12      0.17      4795
           3       0.35      0.14      0.20      7198
           4       0.39      0.45      0.42     14270
           5       0.56      0.71      0.62     19959

    accuracy                           0.48     52336
   macro avg       0.43      0.39      0.39     52336
weighted avg       0.46      0.48      0.46     52336


get_confusion_matrix(LR, X_test, Y_test)

Confusion matrix, without normalization
[[ 3159   448   327   829  1351]
 [ 1031   552   594  1318  1300]
 [  542   374  1025  3080  2177]
 [  412   198   682  6426  6552]
 [  462   189   338  4753 14217]]
Normalized confusion matrix
[[0.51668302 0.07327445 0.05348381 0.13559045 0.22096827]
 [0.21501564 0.11511992 0.12387904 0.27486966 0.27111575]
 [0.07529869 0.05195888 0.14240067 0.42789664 0.30244512]
 [0.02887176 0.01387526 0.04779257 0.45031535 0.45914506]
 [0.02314745 0.00946941 0.01693472 0.23813818 0.71231024]]


columns_to_keep = ['stars_x', 'keywords']
df_second_model = nightlife_businesses_PA[columns_to_keep]
df_second_model


nan_count = np.count_nonzero(df_second_model.isna())
print(nan_count)

0


df_second_model['stars_x'] = df_second_model.apply(transform_ratings, axis=1)


df_second_model


df_second_model["stars_x"].value_counts()

1    172155
0     89523
Name: stars_x, dtype: int64


stars_ratings = [df_second_model['stars_x'] for review in df_second_model]
stars_ratings

[0         1
 1         0
 2         1
 3         1
 4         1
          ..
 261673    0
 261674    1
 261675    1
 261676    1
 261677    1
 Name: stars_x, Length: 261678, dtype: int64,
 0         1
 1         0
 2         1
 3         1
 4         1
          ..
 261673    0
 261674    1
 261675    1
 261676    1
 261677    1
 Name: stars_x, Length: 261678, dtype: int64]


keywords = df_second_model['keywords'] 
keywords

0         ['Tremendous', 'service', 'Big', 'shout', 'Dou...
1         ['good', 'place', 'lofty', 'prices', 'proporti...
2         ['bar', 'area', 'upscale', 'cities', 'restaura...
3         ['prime', 'rib', 'steak', 'joints', 'experience']
4         ['name', 'Best', 'Prime', 'Rib', 'town', 'Serv...
                                ...                        
261673    ['family', 'special', 'occasion', 'help', 'dis...
261674                                     ['open', 'spot']
261675    ['beautiful', 'birthday', 'Lark', 'staff', 'de...
261676                    ['good', 'Lark', 'anyone', 'get']
261677    ['food', 'perfect', 'rare', 'restaurant', 'goo...
Name: keywords, Length: 261678, dtype: object


# This vectorizer breaks text into single words and uni-grams
# and then calculates the TF-IDF representation
vectorizer = TfidfVectorizer(ngram_range=(1,1))

# the 'fit' builds up the vocabulary from all the reviews
# while the 'transform' step turns each indivdual text into
# a matrix of numbers.
vectors = vectorizer.fit_transform(keywords)


X_train, X_test, Y_train, Y_test = train_test_split(vectors, stars_ratings[1], test_size=0.20, random_state=seed, 
                                                     stratify= stars_ratings[1])


classifiers = [
    LogisticRegression(),
    MultinomialNB(),
    RandomForestClassifier(),
    LinearSVC()
    ]

get_models_score(classifiers, X_train, Y_train)

LogisticRegression()
model score: 0.773
Accuracy: 0.7730243044940385
MultinomialNB()
model score: 0.755
Accuracy: 0.7547386120452461
RandomForestClassifier()
model score: 0.757
Accuracy: 0.7571079180678691
LinearSVC()
model score: 0.765
Accuracy: 0.7648845918679303


LR = LogisticRegression()


LR = LogisticRegression(penalty='l2', C=1.0, random_state=seed, n_jobs=2)

LR.fit(X_train, Y_train)
y_pred = LR.predict(X_test)

print("model score: %.3f" % LR.score(X_test, Y_test))
print("Accuracy:",metrics.accuracy_score(Y_test, y_pred))

<frozen importlib._bootstrap>:228: RuntimeWarning: scipy._lib.messagestream.MessageStream size changed, may indicate binary incompatibility. Expected 56 from C header, got 64 from PyObject

model score: 0.774
Accuracy: 0.7735402017731581

/Users/ignaciogonzalez/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(


get_model_report(Y_test, y_pred)

Classification Report
              precision    recall  f1-score   support

           0       0.73      0.53      0.62     17905
           1       0.79      0.90      0.84     34431

    accuracy                           0.77     52336
   macro avg       0.76      0.72      0.73     52336
weighted avg       0.77      0.77      0.76     52336


get_confusion_matrix(LR, X_test, Y_test)

Confusion matrix, without normalization
[[ 9493  8412]
 [ 3440 30991]]
Normalized confusion matrix
[[0.5301871  0.4698129 ]
 [0.09990996 0.90009004]]


prob_predictions = LR.predict_proba(X_test)


yhat = prob_predictions[:, 1]


get_roc_curves(Y_test, yhat)

Best Threshold=0.660704, G-Mean=0.743


precision, recall, thresholds = precision_recall_curve(Y_test, yhat)


get_precission_recall_curve(Y_test)

Best Threshold=0.435148, F-Score=0.843


score = f1_score(Y_test, y_pred)
print('F-Score: %.5f' % score)

F-Score: 0.83948


skplt.metrics.plot_cumulative_gain(Y_test, prob_predictions)
plt.show()


skplt.metrics.plot_lift_curve(Y_test, prob_predictions)
plt.show()


fbeta_score(Y_test, y_pred, beta=beta)

0.8748242786249415


oversampler = RandomOverSampler(sampling_strategy='minority')


X_train, X_test, Y_train, Y_test = train_test_split(vectors, stars_ratings[1], test_size=0.20, random_state=seed, 
                                                    stratify= stars_ratings[1])


X_train_oversampled, y_train_oversampled = oversampler.fit_resample(X_train, Y_train)


y_train_oversampled

0         1
1         0
2         1
3         1
4         0
         ..
275443    0
275444    0
275445    0
275446    0
275447    0
Name: stars_x, Length: 275448, dtype: int64


classifiers = [
    LogisticRegression(),
    MultinomialNB(),
    RandomForestClassifier(),
    LinearSVC()
    ]

get_models_score(classifiers, X_train_oversampled, y_train_oversampled)

LogisticRegression()
model score: 0.734
Accuracy: 0.7344084377866096
MultinomialNB()
model score: 0.733
Accuracy: 0.73318557016203
RandomForestClassifier()
model score: 0.745
Accuracy: 0.7446690614490982
LinearSVC()
model score: 0.727
Accuracy: 0.727128553959034


clf = RandomForestClassifier()

clf.fit(X_train_oversampled, y_train_oversampled)
y_pred = clf.predict(X_test)

print("model score: %.3f" % clf.score(X_test, Y_test))
print("Accuracy:",metrics.accuracy_score(Y_test, y_pred))

model score: 0.748
Accuracy: 0.7481848058697645


get_model_report(Y_test, y_pred)

Classification Report
              precision    recall  f1-score   support

           0       0.64      0.60      0.62     17905
           1       0.80      0.83      0.81     34431

    accuracy                           0.75     52336
   macro avg       0.72      0.71      0.72     52336
weighted avg       0.74      0.75      0.75     52336


get_confusion_matrix(clf, X_test, Y_test)

Confusion matrix, without normalization
[[10672  7233]
 [ 5946 28485]]
Normalized confusion matrix
[[0.59603463 0.40396537]
 [0.17269321 0.82730679]]


prob_predictions = clf.predict_proba(X_test)


yhat = prob_predictions[:, 1]


get_roc_curves(Y_test, yhat)

Best Threshold=0.610588, G-Mean=0.722


precision, recall, thresholds = precision_recall_curve(Y_test, yhat)


get_precission_recall_curve(Y_test)

Best Threshold=0.350190, F-Score=0.830


score = f1_score(Y_test, y_pred)
print('F-Score: %.5f' % score)

F-Score: 0.81213


skplt.metrics.plot_cumulative_gain(Y_test, prob_predictions)
plt.show()


skplt.metrics.plot_lift_curve(Y_test, prob_predictions)
plt.show()


fbeta_score(Y_test, y_pred, beta=beta)

0.821167883211679


X_test

<52336x24548 sparse matrix of type '<class 'numpy.float64'>'
	with 267462 stored elements in Compressed Sparse Row format>


feature_names = vectorizer.get_feature_names_out()


explainer = shap.Explainer(LR, X_train)
shap_values = explainer.shap_values(X_test)


colour_test = pd.DataFrame(X_test.todense())


shap.summary_plot(shap_values, colour_test, feature_names=feature_names)


shap.initjs()
shap.force_plot(explainer.expected_value, shap_values[5,:], 
                colour_test.iloc[5,:], feature_names=feature_names)


shap.initjs()
shap.force_plot(explainer.expected_value, shap_values[2760,:], 
                colour_test.iloc[2760,:], feature_names=feature_names)

	review_id	business_id	stars_x	useful	funny	cool	date	keywords	BusinessAcceptsCreditCards	RestaurantsDelivery	OutdoorSeating	BikeParking	RestaurantsPriceRange2	RestaurantsTakeOut	ByAppointmentOnly	WiFi	Alcohol	Caters	WheelchairAccessible	GoodForKids	RestaurantsAttire	RestaurantsReservations	CoatCheck	DogsAllowed	RestaurantsTableService	RestaurantsGoodForGroups	HasTV	HappyHour	DriveThru	NoiseLevel	BusinessAcceptsBitcoin	AcceptsInsurance	Smoking	GoodForDancing	BYOB	Corkage	BYOBCorkage	HairSpecializesIn	Open24Hours	RestaurantsCounterService	AgesAllowed	DietaryRestrictions	Monday	Tuesday	Wednesday	Thursday	Friday	Saturday	Sunday	name	postal_code	latitude	longitude	stars_y	review_count
0	oyaMhzBSwfGgemSGuZCdwQ	YtSqYv1Q_pOltsVPSx54SA	5.0	0.0	0.0	0.0	2013-06-24 11:21:25	['Tremendous', 'service', 'Big', 'shout', 'Dou...	True	True	False	True	3.0	True	NaN	'free'	'full_bar'	False	True	False	u'dressy'	True	True	NaN	NaN	True	True	True	NaN	u'average'	NaN	NaN	NaN	False	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	16:30-22:0	16:30-22:0	16:30-22:0	16:30-22:0	16:30-22:0	Rittenhouse Grill	19103	39.948949	-75.169532	3.5	290
1	iOQ_bnKI5HfPbH43DMAw6w	YtSqYv1Q_pOltsVPSx54SA	3.0	0.0	0.0	0.0	2013-01-27 19:22:26	['good', 'place', 'lofty', 'prices', 'proporti...	True	True	False	True	3.0	True	NaN	'free'	'full_bar'	False	True	False	u'dressy'	True	True	NaN	NaN	True	True	True	NaN	u'average'	NaN	NaN	NaN	False	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	16:30-22:0	16:30-22:0	16:30-22:0	16:30-22:0	16:30-22:0	Rittenhouse Grill	19103	39.948949	-75.169532	3.5	290
2	rzrBiijeQh7ubjfRCr-UtA	YtSqYv1Q_pOltsVPSx54SA	4.0	12.0	11.0	11.0	2008-04-30 15:26:12	['bar', 'area', 'upscale', 'cities', 'restaura...	True	True	False	True	3.0	True	NaN	'free'	'full_bar'	False	True	False	u'dressy'	True	True	NaN	NaN	True	True	True	NaN	u'average'	NaN	NaN	NaN	False	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	16:30-22:0	16:30-22:0	16:30-22:0	16:30-22:0	16:30-22:0	Rittenhouse Grill	19103	39.948949	-75.169532	3.5	290
3	1HP3yZN3jT646IlHSo7GZw	YtSqYv1Q_pOltsVPSx54SA	5.0	0.0	0.0	0.0	2014-06-11 16:10:04	['prime', 'rib', 'steak', 'joints', 'experience']	True	True	False	True	3.0	True	NaN	'free'	'full_bar'	False	True	False	u'dressy'	True	True	NaN	NaN	True	True	True	NaN	u'average'	NaN	NaN	NaN	False	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	16:30-22:0	16:30-22:0	16:30-22:0	16:30-22:0	16:30-22:0	Rittenhouse Grill	19103	39.948949	-75.169532	3.5	290
4	Vv6acqoztdtzTD8Gq0gifA	YtSqYv1Q_pOltsVPSx54SA	5.0	0.0	0.0	0.0	2018-03-04 00:43:27	['name', 'Best', 'Prime', 'Rib', 'town', 'Serv...	True	True	False	True	3.0	True	NaN	'free'	'full_bar'	False	True	False	u'dressy'	True	True	NaN	NaN	True	True	True	NaN	u'average'	NaN	NaN	NaN	False	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	16:30-22:0	16:30-22:0	16:30-22:0	16:30-22:0	16:30-22:0	Rittenhouse Grill	19103	39.948949	-75.169532	3.5	290

	review_id	business_id	stars_x	useful	funny	cool	date	keywords	BusinessAcceptsCreditCards	RestaurantsDelivery	OutdoorSeating	BikeParking	RestaurantsPriceRange2	RestaurantsTakeOut	ByAppointmentOnly	WiFi	Alcohol	Caters	WheelchairAccessible	GoodForKids	RestaurantsAttire	RestaurantsReservations	CoatCheck	DogsAllowed	RestaurantsTableService	RestaurantsGoodForGroups	HasTV	HappyHour	DriveThru	NoiseLevel	BusinessAcceptsBitcoin	AcceptsInsurance	Smoking	GoodForDancing	BYOB	Corkage	BYOBCorkage	HairSpecializesIn	Open24Hours	RestaurantsCounterService	AgesAllowed	DietaryRestrictions	Monday	Tuesday	Wednesday	Thursday	Friday	Saturday	Sunday	name	postal_code	latitude	longitude	stars_y	review_count
0	oyaMhzBSwfGgemSGuZCdwQ	YtSqYv1Q_pOltsVPSx54SA	5	0.0	0.0	0.0	2013-06-24 11:21:25	['Tremendous', 'service', 'Big', 'shout', 'Dou...	True	True	False	True	3.0	True	NaN	'free'	'full_bar'	False	True	False	u'dressy'	True	True	NaN	NaN	True	True	True	NaN	u'average'	NaN	NaN	NaN	False	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	16:30-22:0	16:30-22:0	16:30-22:0	16:30-22:0	16:30-22:0	Rittenhouse Grill	19103	39.948949	-75.169532	3.5	290
1	iOQ_bnKI5HfPbH43DMAw6w	YtSqYv1Q_pOltsVPSx54SA	3	0.0	0.0	0.0	2013-01-27 19:22:26	['good', 'place', 'lofty', 'prices', 'proporti...	True	True	False	True	3.0	True	NaN	'free'	'full_bar'	False	True	False	u'dressy'	True	True	NaN	NaN	True	True	True	NaN	u'average'	NaN	NaN	NaN	False	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	16:30-22:0	16:30-22:0	16:30-22:0	16:30-22:0	16:30-22:0	Rittenhouse Grill	19103	39.948949	-75.169532	3.5	290
2	rzrBiijeQh7ubjfRCr-UtA	YtSqYv1Q_pOltsVPSx54SA	4	12.0	11.0	11.0	2008-04-30 15:26:12	['bar', 'area', 'upscale', 'cities', 'restaura...	True	True	False	True	3.0	True	NaN	'free'	'full_bar'	False	True	False	u'dressy'	True	True	NaN	NaN	True	True	True	NaN	u'average'	NaN	NaN	NaN	False	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	16:30-22:0	16:30-22:0	16:30-22:0	16:30-22:0	16:30-22:0	Rittenhouse Grill	19103	39.948949	-75.169532	3.5	290
3	1HP3yZN3jT646IlHSo7GZw	YtSqYv1Q_pOltsVPSx54SA	5	0.0	0.0	0.0	2014-06-11 16:10:04	['prime', 'rib', 'steak', 'joints', 'experience']	True	True	False	True	3.0	True	NaN	'free'	'full_bar'	False	True	False	u'dressy'	True	True	NaN	NaN	True	True	True	NaN	u'average'	NaN	NaN	NaN	False	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	16:30-22:0	16:30-22:0	16:30-22:0	16:30-22:0	16:30-22:0	Rittenhouse Grill	19103	39.948949	-75.169532	3.5	290
4	Vv6acqoztdtzTD8Gq0gifA	YtSqYv1Q_pOltsVPSx54SA	5	0.0	0.0	0.0	2018-03-04 00:43:27	['name', 'Best', 'Prime', 'Rib', 'town', 'Serv...	True	True	False	True	3.0	True	NaN	'free'	'full_bar'	False	True	False	u'dressy'	True	True	NaN	NaN	True	True	True	NaN	u'average'	NaN	NaN	NaN	False	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	16:30-22:0	16:30-22:0	16:30-22:0	16:30-22:0	16:30-22:0	Rittenhouse Grill	19103	39.948949	-75.169532	3.5	290
5	8CF6b3nrnAohDawiKv87TA	YtSqYv1Q_pOltsVPSx54SA	5	0.0	0.0	0.0	2016-02-23 19:40:10	['Prime', 'Rib', 'offer', 'cut']	True	True	False	True	3.0	True	NaN	'free'	'full_bar'	False	True	False	u'dressy'	True	True	NaN	NaN	True	True	True	NaN	u'average'	NaN	NaN	NaN	False	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	16:30-22:0	16:30-22:0	16:30-22:0	16:30-22:0	16:30-22:0	Rittenhouse Grill	19103	39.948949	-75.169532	3.5	290
6	E6qHEOzFhGiYAn5cgzbZkQ	YtSqYv1Q_pOltsVPSx54SA	2	3.0	0.0	0.0	2010-07-07 21:29:29	['nothing', 'thats', 'food']	True	True	False	True	3.0	True	NaN	'free'	'full_bar'	False	True	False	u'dressy'	True	True	NaN	NaN	True	True	True	NaN	u'average'	NaN	NaN	NaN	False	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	16:30-22:0	16:30-22:0	16:30-22:0	16:30-22:0	16:30-22:0	Rittenhouse Grill	19103	39.948949	-75.169532	3.5	290
7	vftJTlt7KVMtCRbcBZNSwg	YtSqYv1Q_pOltsVPSx54SA	5	1.0	0.0	0.0	2014-12-18 20:47:25	['time', 'cut', 'wine', 'full', 'boyfriend']	True	True	False	True	3.0	True	NaN	'free'	'full_bar'	False	True	False	u'dressy'	True	True	NaN	NaN	True	True	True	NaN	u'average'	NaN	NaN	NaN	False	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	16:30-22:0	16:30-22:0	16:30-22:0	16:30-22:0	16:30-22:0	Rittenhouse Grill	19103	39.948949	-75.169532	3.5	290
8	wdcjv9W7RodJofnvBzK6FQ	YtSqYv1Q_pOltsVPSx54SA	5	1.0	0.0	0.0	2014-09-10 13:25:46	['friend', 'food', 'best', 'Phila', 'night']	True	True	False	True	3.0	True	NaN	'free'	'full_bar'	False	True	False	u'dressy'	True	True	NaN	NaN	True	True	True	NaN	u'average'	NaN	NaN	NaN	False	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	16:30-22:0	16:30-22:0	16:30-22:0	16:30-22:0	16:30-22:0	Rittenhouse Grill	19103	39.948949	-75.169532	3.5	290
9	nd29xztgxcH1cV0Srf9WAw	YtSqYv1Q_pOltsVPSx54SA	1	2.0	3.0	0.0	2016-10-02 00:32:34	['place', 'negative', 'stars']	True	True	False	True	3.0	True	NaN	'free'	'full_bar'	False	True	False	u'dressy'	True	True	NaN	NaN	True	True	True	NaN	u'average'	NaN	NaN	NaN	False	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	16:30-22:0	16:30-22:0	16:30-22:0	16:30-22:0	16:30-22:0	Rittenhouse Grill	19103	39.948949	-75.169532	3.5	290
10	IY7i04LMohgO7HIk5pYvew	YtSqYv1Q_pOltsVPSx54SA	5	2.0	2.0	2.0	2005-09-23 13:05:56	['fact', 'part', 'small']	True	True	False	True	3.0	True	NaN	'free'	'full_bar'	False	True	False	u'dressy'	True	True	NaN	NaN	True	True	True	NaN	u'average'	NaN	NaN	NaN	False	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	16:30-22:0	16:30-22:0	16:30-22:0	16:30-22:0	16:30-22:0	Rittenhouse Grill	19103	39.948949	-75.169532	3.5	290
11	r0Rg8B4XDsBf8KomsPijmw	YtSqYv1Q_pOltsVPSx54SA	5	2.0	1.0	0.0	2013-01-28 02:15:10	['restaurant', 'week', 'place', 'years', 'times']	True	True	False	True	3.0	True	NaN	'free'	'full_bar'	False	True	False	u'dressy'	True	True	NaN	NaN	True	True	True	NaN	u'average'	NaN	NaN	NaN	False	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	16:30-22:0	16:30-22:0	16:30-22:0	16:30-22:0	16:30-22:0	Rittenhouse Grill	19103	39.948949	-75.169532	3.5	290
12	RWSb10J0em22lTpFhZ5QZQ	YtSqYv1Q_pOltsVPSx54SA	3	0.0	0.0	0.0	2017-11-07 14:03:03	['crab', 'food', 'fine', 'cakes']	True	True	False	True	3.0	True	NaN	'free'	'full_bar'	False	True	False	u'dressy'	True	True	NaN	NaN	True	True	True	NaN	u'average'	NaN	NaN	NaN	False	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	16:30-22:0	16:30-22:0	16:30-22:0	16:30-22:0	16:30-22:0	Rittenhouse Grill	19103	39.948949	-75.169532	3.5	290
13	A50zoocWps2aiZzbDCk6Iw	YtSqYv1Q_pOltsVPSx54SA	2	5.0	2.0	4.0	2009-08-07 15:17:30	['good', 'fancy', 'half', 'cut']	True	True	False	True	3.0	True	NaN	'free'	'full_bar'	False	True	False	u'dressy'	True	True	NaN	NaN	True	True	True	NaN	u'average'	NaN	NaN	NaN	False	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	16:30-22:0	16:30-22:0	16:30-22:0	16:30-22:0	16:30-22:0	Rittenhouse Grill	19103	39.948949	-75.169532	3.5	290
14	UBSnRKNmhyKdx-PTn8abUA	YtSqYv1Q_pOltsVPSx54SA	2	1.0	0.0	0.0	2013-08-16 04:34:42	['way', 'food', 'date', 'place', 'server', 'me...	True	True	False	True	3.0	True	NaN	'free'	'full_bar'	False	True	False	u'dressy'	True	True	NaN	NaN	True	True	True	NaN	u'average'	NaN	NaN	NaN	False	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	16:30-22:0	16:30-22:0	16:30-22:0	16:30-22:0	16:30-22:0	Rittenhouse Grill	19103	39.948949	-75.169532	3.5	290
15	D599DF6m2Z6XC7R1e4xHkg	YtSqYv1Q_pOltsVPSx54SA	5	1.0	1.0	0.0	2017-02-20 03:56:36	['salad', 'prime', 'rib', 'delicious']	True	True	False	True	3.0	True	NaN	'free'	'full_bar'	False	True	False	u'dressy'	True	True	NaN	NaN	True	True	True	NaN	u'average'	NaN	NaN	NaN	False	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	16:30-22:0	16:30-22:0	16:30-22:0	16:30-22:0	16:30-22:0	Rittenhouse Grill	19103	39.948949	-75.169532	3.5	290
16	q1bg-VKtl0YHpqwOYfbTzg	YtSqYv1Q_pOltsVPSx54SA	4	1.0	0.0	0.0	2016-01-20 02:08:26	['great', 'prime', 'rib', 'month', 'chocolate']	True	True	False	True	3.0	True	NaN	'free'	'full_bar'	False	True	False	u'dressy'	True	True	NaN	NaN	True	True	True	NaN	u'average'	NaN	NaN	NaN	False	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	16:30-22:0	16:30-22:0	16:30-22:0	16:30-22:0	16:30-22:0	Rittenhouse Grill	19103	39.948949	-75.169532	3.5	290
17	mo3pqyAumP4BG4Nz48FiwA	YtSqYv1Q_pOltsVPSx54SA	4	0.0	0.0	0.0	2016-06-25 12:10:26	['BAR', 'expensive', 'worth', 'account']	True	True	False	True	3.0	True	NaN	'free'	'full_bar'	False	True	False	u'dressy'	True	True	NaN	NaN	True	True	True	NaN	u'average'	NaN	NaN	NaN	False	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	16:30-22:0	16:30-22:0	16:30-22:0	16:30-22:0	16:30-22:0	Rittenhouse Grill	19103	39.948949	-75.169532	3.5	290
18	jZizwcu-gH9MYrLpXOcauw	YtSqYv1Q_pOltsVPSx54SA	5	0.0	0.0	0.0	2017-12-30 22:53:46	['fantastic', 'wonderful', 'style', 'steakhous...	True	True	False	True	3.0	True	NaN	'free'	'full_bar'	False	True	False	u'dressy'	True	True	NaN	NaN	True	True	True	NaN	u'average'	NaN	NaN	NaN	False	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	16:30-22:0	16:30-22:0	16:30-22:0	16:30-22:0	16:30-22:0	Rittenhouse Grill	19103	39.948949	-75.169532	3.5	290
19	UG8HGpvSl8GWlZnHAUHZPg	YtSqYv1Q_pOltsVPSx54SA	5	0.0	2.0	1.0	2014-05-28 12:21:15	['food', 'kids']	True	True	False	True	3.0	True	NaN	'free'	'full_bar'	False	True	False	u'dressy'	True	True	NaN	NaN	True	True	True	NaN	u'average'	NaN	NaN	NaN	False	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	16:30-22:0	16:30-22:0	16:30-22:0	16:30-22:0	16:30-22:0	Rittenhouse Grill	19103	39.948949	-75.169532	3.5	290
20	GmqFBtJkxbh1UjTDPnBJJQ	YtSqYv1Q_pOltsVPSx54SA	2	0.0	0.0	0.0	2017-12-04 14:22:57	['nice', 'service', 'time', 'Philly', 'OK']	True	True	False	True	3.0	True	NaN	'free'	'full_bar'	False	True	False	u'dressy'	True	True	NaN	NaN	True	True	True	NaN	u'average'	NaN	NaN	NaN	False	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	16:30-22:0	16:30-22:0	16:30-22:0	16:30-22:0	16:30-22:0	Rittenhouse Grill	19103	39.948949	-75.169532	3.5	290
21	BqtVaxlau4qV8cqtEFDd4w	YtSqYv1Q_pOltsVPSx54SA	3	2.0	2.0	2.0	2009-10-26 04:41:23	['steak', 'please', 'medium', 'rare', 'LOVE']	True	True	False	True	3.0	True	NaN	'free'	'full_bar'	False	True	False	u'dressy'	True	True	NaN	NaN	True	True	True	NaN	u'average'	NaN	NaN	NaN	False	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	16:30-22:0	16:30-22:0	16:30-22:0	16:30-22:0	16:30-22:0	Rittenhouse Grill	19103	39.948949	-75.169532	3.5	290
22	RvZHRve8_DHD957g2Mlpwg	YtSqYv1Q_pOltsVPSx54SA	4	0.0	0.0	0.0	2012-04-01 20:25:52	['good', 'business', 'review', 'old', 'money',...	True	True	False	True	3.0	True	NaN	'free'	'full_bar'	False	True	False	u'dressy'	True	True	NaN	NaN	True	True	True	NaN	u'average'	NaN	NaN	NaN	False	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	16:30-22:0	16:30-22:0	16:30-22:0	16:30-22:0	16:30-22:0	Rittenhouse Grill	19103	39.948949	-75.169532	3.5	290
23	cvExhHIpO4fXilY9hGi5Kw	YtSqYv1Q_pOltsVPSx54SA	1	0.0	0.0	0.0	2015-09-02 01:04:11	['general', 'manager', 'disrespectful', 'food'...	True	True	False	True	3.0	True	NaN	'free'	'full_bar'	False	True	False	u'dressy'	True	True	NaN	NaN	True	True	True	NaN	u'average'	NaN	NaN	NaN	False	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	16:30-22:0	16:30-22:0	16:30-22:0	16:30-22:0	16:30-22:0	Rittenhouse Grill	19103	39.948949	-75.169532	3.5	290
24	rFEvx_pu0Y0JtEZuzBzTQA	YtSqYv1Q_pOltsVPSx54SA	4	0.0	0.0	0.0	2014-09-29 13:48:42	['tender', 'good', 'place', 'look']	True	True	False	True	3.0	True	NaN	'free'	'full_bar'	False	True	False	u'dressy'	True	True	NaN	NaN	True	True	True	NaN	u'average'	NaN	NaN	NaN	False	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	16:30-22:0	16:30-22:0	16:30-22:0	16:30-22:0	16:30-22:0	Rittenhouse Grill	19103	39.948949	-75.169532	3.5	290
25	XdP_JZI9jESTPqW8YBWf-Q	YtSqYv1Q_pOltsVPSx54SA	5	0.0	0.0	0.0	2012-04-04 10:02:09	['trip', 'Prime', 'Rib', 'meal', 'selection', ...	True	True	False	True	3.0	True	NaN	'free'	'full_bar'	False	True	False	u'dressy'	True	True	NaN	NaN	True	True	True	NaN	u'average'	NaN	NaN	NaN	False	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	16:30-22:0	16:30-22:0	16:30-22:0	16:30-22:0	16:30-22:0	Rittenhouse Grill	19103	39.948949	-75.169532	3.5	290
26	I6nS0j-m1ifQHWq4C2mzLg	YtSqYv1Q_pOltsVPSx54SA	5	4.0	2.0	3.0	2011-01-18 20:27:05	['steak', 'prime', 'rib', 'Philly', 'good']	True	True	False	True	3.0	True	NaN	'free'	'full_bar'	False	True	False	u'dressy'	True	True	NaN	NaN	True	True	True	NaN	u'average'	NaN	NaN	NaN	False	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	16:30-22:0	16:30-22:0	16:30-22:0	16:30-22:0	16:30-22:0	Rittenhouse Grill	19103	39.948949	-75.169532	3.5	290
27	I1IKWTGEZF1vxFNo_G8f5A	YtSqYv1Q_pOltsVPSx54SA	5	0.0	0.0	0.0	2015-05-02 02:13:32	['Best', 'Prime', 'rib', 'Everything', 'Love',...	True	True	False	True	3.0	True	NaN	'free'	'full_bar'	False	True	False	u'dressy'	True	True	NaN	NaN	True	True	True	NaN	u'average'	NaN	NaN	NaN	False	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	16:30-22:0	16:30-22:0	16:30-22:0	16:30-22:0	16:30-22:0	Rittenhouse Grill	19103	39.948949	-75.169532	3.5	290
28	NW8H4MU1uz-xx18CZJKy7A	YtSqYv1Q_pOltsVPSx54SA	5	0.0	0.0	0.0	2012-09-16 22:34:05	['nights', 'favorite', 'prime', 'rib', 'potato']	True	True	False	True	3.0	True	NaN	'free'	'full_bar'	False	True	False	u'dressy'	True	True	NaN	NaN	True	True	True	NaN	u'average'	NaN	NaN	NaN	False	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	16:30-22:0	16:30-22:0	16:30-22:0	16:30-22:0	16:30-22:0	Rittenhouse Grill	19103	39.948949	-75.169532	3.5	290
29	-5Ej9ImJ51-WCmX4fyvETA	YtSqYv1Q_pOltsVPSx54SA	2	0.0	3.0	0.0	2013-05-31 12:20:34	['full', 'old', 'men', 'Tony', 'Soprano', 'liv...	True	True	False	True	3.0	True	NaN	'free'	'full_bar'	False	True	False	u'dressy'	True	True	NaN	NaN	True	True	True	NaN	u'average'	NaN	NaN	NaN	False	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	16:30-22:0	16:30-22:0	16:30-22:0	16:30-22:0	16:30-22:0	Rittenhouse Grill	19103	39.948949	-75.169532	3.5	290
30	OUWmGzDpvFbW3MdZkVeDNQ	YtSqYv1Q_pOltsVPSx54SA	3	0.0	0.0	0.0	2013-08-05 12:54:01	['good', 'wearing', 'waiter', 'table', 'captai...	True	True	False	True	3.0	True	NaN	'free'	'full_bar'	False	True	False	u'dressy'	True	True	NaN	NaN	True	True	True	NaN	u'average'	NaN	NaN	NaN	False	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	16:30-22:0	16:30-22:0	16:30-22:0	16:30-22:0	16:30-22:0	Rittenhouse Grill	19103	39.948949	-75.169532	3.5	290
31	mQ0YpErPdsHTd2KXPBhF2Q	YtSqYv1Q_pOltsVPSx54SA	1	0.0	0.0	0.0	2016-03-19 01:10:23	['mice', 'scurry', 'floor', 'server']	True	True	False	True	3.0	True	NaN	'free'	'full_bar'	False	True	False	u'dressy'	True	True	NaN	NaN	True	True	True	NaN	u'average'	NaN	NaN	NaN	False	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	16:30-22:0	16:30-22:0	16:30-22:0	16:30-22:0	16:30-22:0	Rittenhouse Grill	19103	39.948949	-75.169532	3.5	290
32	W0rfL71LsLlV7Jl6SEoR6Q	YtSqYv1Q_pOltsVPSx54SA	4	0.0	0.0	0.0	2011-10-07 13:32:08	['food', 'Antonio']	True	True	False	True	3.0	True	NaN	'free'	'full_bar'	False	True	False	u'dressy'	True	True	NaN	NaN	True	True	True	NaN	u'average'	NaN	NaN	NaN	False	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	16:30-22:0	16:30-22:0	16:30-22:0	16:30-22:0	16:30-22:0	Rittenhouse Grill	19103	39.948949	-75.169532	3.5	290
33	ZdIu2b27brSWFqG_pJDLeA	YtSqYv1Q_pOltsVPSx54SA	5	4.0	1.0	2.0	2014-04-03 15:59:19	['wife', 'rehearsal', 'dinner', 'wedding']	True	True	False	True	3.0	True	NaN	'free'	'full_bar'	False	True	False	u'dressy'	True	True	NaN	NaN	True	True	True	NaN	u'average'	NaN	NaN	NaN	False	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	16:30-22:0	16:30-22:0	16:30-22:0	16:30-22:0	16:30-22:0	Rittenhouse Grill	19103	39.948949	-75.169532	3.5	290
34	3gFGEmRwa9cjgmcgyU8TlQ	YtSqYv1Q_pOltsVPSx54SA	4	0.0	0.0	0.0	2010-11-27 04:26:33	['pleased', 'table', 'shrimp', 'salad', 'dinner']	True	True	False	True	3.0	True	NaN	'free'	'full_bar'	False	True	False	u'dressy'	True	True	NaN	NaN	True	True	True	NaN	u'average'	NaN	NaN	NaN	False	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	16:30-22:0	16:30-22:0	16:30-22:0	16:30-22:0	16:30-22:0	Rittenhouse Grill	19103	39.948949	-75.169532	3.5	290
35	B3bB1EVtODLW-15qBGgKFA	YtSqYv1Q_pOltsVPSx54SA	5	0.0	0.0	0.0	2010-05-17 14:58:28	['great', 'family', 'Prime', 'Rib', 'meal', 'E...	True	True	False	True	3.0	True	NaN	'free'	'full_bar'	False	True	False	u'dressy'	True	True	NaN	NaN	True	True	True	NaN	u'average'	NaN	NaN	NaN	False	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	16:30-22:0	16:30-22:0	16:30-22:0	16:30-22:0	16:30-22:0	Rittenhouse Grill	19103	39.948949	-75.169532	3.5	290
36	-nxuCi0p3qv1s8DkX2kINg	YtSqYv1Q_pOltsVPSx54SA	3	0.0	0.0	0.0	2016-06-01 22:28:06	['good', 'Prime', 'Rib', 'era', 'service', 'Sa...	True	True	False	True	3.0	True	NaN	'free'	'full_bar'	False	True	False	u'dressy'	True	True	NaN	NaN	True	True	True	NaN	u'average'	NaN	NaN	NaN	False	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	16:30-22:0	16:30-22:0	16:30-22:0	16:30-22:0	16:30-22:0	Rittenhouse Grill	19103	39.948949	-75.169532	3.5	290
37	X2CTpfyhB6S7WnDDpPnOdg	YtSqYv1Q_pOltsVPSx54SA	3	1.0	0.0	0.0	2016-01-21 00:06:56	['service', 'bad', 'lamb', 'rack', 'good']	True	True	False	True	3.0	True	NaN	'free'	'full_bar'	False	True	False	u'dressy'	True	True	NaN	NaN	True	True	True	NaN	u'average'	NaN	NaN	NaN	False	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	16:30-22:0	16:30-22:0	16:30-22:0	16:30-22:0	16:30-22:0	Rittenhouse Grill	19103	39.948949	-75.169532	3.5	290
38	9JpxZpcDPTGqIEBoz0IUBg	YtSqYv1Q_pOltsVPSx54SA	4	2.0	1.0	0.0	2015-02-05 15:12:55	['place', 'service', 'Juicy', 'prime', 'rib']	True	True	False	True	3.0	True	NaN	'free'	'full_bar'	False	True	False	u'dressy'	True	True	NaN	NaN	True	True	True	NaN	u'average'	NaN	NaN	NaN	False	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	16:30-22:0	16:30-22:0	16:30-22:0	16:30-22:0	16:30-22:0	Rittenhouse Grill	19103	39.948949	-75.169532	3.5	290
39	p2SBD6gyw3djvrSh_Daz4Q	YtSqYv1Q_pOltsVPSx54SA	4	3.0	1.0	4.0	2008-09-18 18:35:40	['Prime', 'Rib', 'Restaurant', 'Week']	True	True	False	True	3.0	True	NaN	'free'	'full_bar'	False	True	False	u'dressy'	True	True	NaN	NaN	True	True	True	NaN	u'average'	NaN	NaN	NaN	False	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	16:30-22:0	16:30-22:0	16:30-22:0	16:30-22:0	16:30-22:0	Rittenhouse Grill	19103	39.948949	-75.169532	3.5	290
40	QL8e1L1Vf1KDwoG2khGNGQ	YtSqYv1Q_pOltsVPSx54SA	4	0.0	0.0	0.0	2018-04-29 12:19:46	['restaurant', 'place', 'Saturday', 'evening']	True	True	False	True	3.0	True	NaN	'free'	'full_bar'	False	True	False	u'dressy'	True	True	NaN	NaN	True	True	True	NaN	u'average'	NaN	NaN	NaN	False	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	16:30-22:0	16:30-22:0	16:30-22:0	16:30-22:0	16:30-22:0	Rittenhouse Grill	19103	39.948949	-75.169532	3.5	290
41	5NrWXIeE_7LpF4moSpxtAA	YtSqYv1Q_pOltsVPSx54SA	5	0.0	0.0	0.0	2011-08-26 00:03:01	['Warwick', 'restaurant', 'nights', 'dinner']	True	True	False	True	3.0	True	NaN	'free'	'full_bar'	False	True	False	u'dressy'	True	True	NaN	NaN	True	True	True	NaN	u'average'	NaN	NaN	NaN	False	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	16:30-22:0	16:30-22:0	16:30-22:0	16:30-22:0	16:30-22:0	Rittenhouse Grill	19103	39.948949	-75.169532	3.5	290
42	a90idd_P1oNf103f-nCzWg	YtSqYv1Q_pOltsVPSx54SA	4	0.0	0.0	0.0	2012-02-05 13:16:55	['steak', 'good', 'see', 'quality', 'sides', '...	True	True	False	True	3.0	True	NaN	'free'	'full_bar'	False	True	False	u'dressy'	True	True	NaN	NaN	True	True	True	NaN	u'average'	NaN	NaN	NaN	False	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	16:30-22:0	16:30-22:0	16:30-22:0	16:30-22:0	16:30-22:0	Rittenhouse Grill	19103	39.948949	-75.169532	3.5	290
43	qCvfeguEDTt1rCHlXp1BdA	YtSqYv1Q_pOltsVPSx54SA	5	3.0	0.0	0.0	2014-09-27 21:25:32	['service', 'bar', 'love', 'Prime', 'Rib', 'El...	True	True	False	True	3.0	True	NaN	'free'	'full_bar'	False	True	False	u'dressy'	True	True	NaN	NaN	True	True	True	NaN	u'average'	NaN	NaN	NaN	False	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	16:30-22:0	16:30-22:0	16:30-22:0	16:30-22:0	16:30-22:0	Rittenhouse Grill	19103	39.948949	-75.169532	3.5	290
44	jb_ZS62r2RO7wwFkPXTzjQ	YtSqYv1Q_pOltsVPSx54SA	4	1.0	0.0	0.0	2010-11-28 16:50:51	['good', 'Make', 'bar', 'start']	True	True	False	True	3.0	True	NaN	'free'	'full_bar'	False	True	False	u'dressy'	True	True	NaN	NaN	True	True	True	NaN	u'average'	NaN	NaN	NaN	False	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	16:30-22:0	16:30-22:0	16:30-22:0	16:30-22:0	16:30-22:0	Rittenhouse Grill	19103	39.948949	-75.169532	3.5	290
45	KnH0ZhP3hlqoGftUwR4eaA	YtSqYv1Q_pOltsVPSx54SA	5	1.0	0.0	0.0	2009-11-09 16:46:15	['Prime', 'Rib', 'sort', 'food']	True	True	False	True	3.0	True	NaN	'free'	'full_bar'	False	True	False	u'dressy'	True	True	NaN	NaN	True	True	True	NaN	u'average'	NaN	NaN	NaN	False	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	16:30-22:0	16:30-22:0	16:30-22:0	16:30-22:0	16:30-22:0	Rittenhouse Grill	19103	39.948949	-75.169532	3.5	290
46	ZhAKHsnlX2r_vrWFNJYZZw	YtSqYv1Q_pOltsVPSx54SA	5	0.0	0.0	0.0	2012-05-11 22:52:33	['Prime', 'Rib', 'DP', 'special']	True	True	False	True	3.0	True	NaN	'free'	'full_bar'	False	True	False	u'dressy'	True	True	NaN	NaN	True	True	True	NaN	u'average'	NaN	NaN	NaN	False	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	16:30-22:0	16:30-22:0	16:30-22:0	16:30-22:0	16:30-22:0	Rittenhouse Grill	19103	39.948949	-75.169532	3.5	290
47	Fw-h3iTbzNC2BpWWQ6arFA	YtSqYv1Q_pOltsVPSx54SA	4	0.0	0.0	1.0	2013-12-10 01:49:09	['place', 'dinner', 'room', 'eat', 'fancy', 'c...	True	True	False	True	3.0	True	NaN	'free'	'full_bar'	False	True	False	u'dressy'	True	True	NaN	NaN	True	True	True	NaN	u'average'	NaN	NaN	NaN	False	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	16:30-22:0	16:30-22:0	16:30-22:0	16:30-22:0	16:30-22:0	Rittenhouse Grill	19103	39.948949	-75.169532	3.5	290
48	HVBOJN5lKvZf3LrjcxTY-A	YtSqYv1Q_pOltsVPSx54SA	4	3.0	0.0	0.0	2016-09-19 18:47:12	['night', 'servers', 'Vinny', 'city']	True	True	False	True	3.0	True	NaN	'free'	'full_bar'	False	True	False	u'dressy'	True	True	NaN	NaN	True	True	True	NaN	u'average'	NaN	NaN	NaN	False	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	16:30-22:0	16:30-22:0	16:30-22:0	16:30-22:0	16:30-22:0	Rittenhouse Grill	19103	39.948949	-75.169532	3.5	290
49	sY6oP1czbvflB2WkLyIZ7g	YtSqYv1Q_pOltsVPSx54SA	5	0.0	0.0	0.0	2018-06-11 04:49:55	['Love', 'feel']	True	True	False	True	3.0	True	NaN	'free'	'full_bar'	False	True	False	u'dressy'	True	True	NaN	NaN	True	True	True	NaN	u'average'	NaN	NaN	NaN	False	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	16:30-22:0	16:30-22:0	16:30-22:0	16:30-22:0	16:30-22:0	Rittenhouse Grill	19103	39.948949	-75.169532	3.5	290

PRACTICA 3 - Business Score Analysis Report¶

3. Modelization¶

Ignacio González - Saúl Segura¶

08/01/2023¶

Libraries¶

Functions¶

Parametrisation¶

Warnings¶

Data¶

Second Model¶

ROC CURVE¶

Precision-Recall Curve¶

Cumulative gains curve¶

Lift curve¶

Oversampled Model¶

ROC CURVE¶

Precision-Recall Curve¶

Cumulative Gains Curve¶

Lift Curve¶

INTERPRETABILITY¶

Unbalanced Model¶

Global Interpretability¶

Local Interpretability¶

	index	percent	stars_x
0	5.0	37.475065	98064
1	4.0	28.313806	74091
2	3.0	13.955701	36519
3	1.0	10.879019	28468
4	2.0	9.376409	24536